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Learn more: PMC Disclaimer | PMC Copyright Notice Taiwan J Ophthalmol . 2025 Oct 24;16(1):68–80. doi: 10.4103/tjo.TJO-D-25-00072 Search in PMC Search in PubMed View in NLM Catalog Add to search Generative artificial intelligence in ophthalmology research writing: A comprehensive review of applications, detection tools, and ethical considerations Pin-Jung Cheng Pin-Jung Cheng 1 College of Medicine, Taipei Medical University, Taipei, Taiwan Find articles by Pin-Jung Cheng 1 , Fang-Yu Hu Fang-Yu Hu 2 College of Medicine, National Taiwan University, Taipei, Taiwan Find articles by Fang-Yu Hu 2 , Le-Yu Chen Le-Yu Chen 3 Department of Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan Find articles by Le-Yu Chen 3 , Jen-Yu Liu Jen-Yu Liu 4 Department of Ophthalmology, Shin Kong Wu Ho Su Memorial Hospital, Taipei, Taiwan Find articles by Jen-Yu Liu 4 , Jo-Hsuan Wu Jo-Hsuan Wu 5 Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA Find articles by Jo-Hsuan Wu 5 , Wei-Li Chen Wei-Li Chen 2 College of Medicine, National Taiwan University, Taipei, Taiwan 3 Department of Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan 6 Advanced Ocular Surface and Corneal Nerve Regeneration Center, National Taiwan University Hospital, Taipei, Taiwan Find articles by Wei-Li Chen 2, 3, 6, * Author information Article notes Copyright and License information 1 College of Medicine, Taipei Medical University, Taipei, Taiwan 2 College of Medicine, National Taiwan University, Taipei, Taiwan 3 Department of Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan 4 Department of Ophthalmology, Shin Kong Wu Ho Su Memorial Hospital, Taipei, Taiwan 5 Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA 6 Advanced Ocular Surface and Corneal Nerve Regeneration Center, National Taiwan University Hospital, Taipei, Taiwan * Address for correspondence: Dr. Wei-Li Chen, Department of Ophthalmology, National Taiwan University Hospital Taipei, Taiwan No. 7, Chung-Shan South Road, Taipei, Taiwan. E-mail: [email protected] Received 2025 Apr 29; Accepted 2025 Jul 18; Collection date 2026 Jan-Mar. Copyright: © 2025 Taiwan J Ophthalmol This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. PMC Copyright notice PMCID: PMC13082807 PMID: 41993659 Abstract The rise of generative artificial intelligence (GenAI) has profoundly influenced medical research and academic writing, particularly in ophthalmology. Despite its growing relevance, there is a noticeable gap in the literature regarding its application in medical writing, including practical uses and associated limitations. This review seeks to fill in this gap by first systematically reviewing the current literature on GenAI in medical paper writing. It identifies and discusses nine key applications and considerations, including idea generation, literature review, institutional review board preparation, data collection, data analysis, image generation, manuscript drafting, writing refinement, and peer review. In the second part, we explore publicly available AI tools that currently assist with medical manuscript writing. We also introduce several generative AI detection tools and discuss their accuracy and reliability. Finally, the review addresses the limitations and ethical challenges associated with the use of GenAI in medical paper writing. While GenAI has streamlined many aspects of medical paper writing, and an increasing number of AI tools have been developed for research, significant model limitations and ethical concerns persist, necessitating careful human oversight and clear guidelines. By providing a comprehensive yet focused overview, this article offers valuable insights into the effective use of GenAI in medical paper writing while acknowledging its limitations and risks. It aims to support researchers in producing high-quality, AI-enhanced publications in the field of ophthalmology. Keywords: Artificial intelligence, generative artificial intelligence, medical writing, ophthalmology Introduction In recent decades, artificial intelligence (AI) has significantly transformed health care, enhancing diagnostic capabilities and advancing image recognition, particularly in ophthalmology.[ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] Traditionally, AI assisted clinicians with diagnostics, image analysis, and improving patient outcomes. However, the advent of generative AI (GenAI), exemplified by models like OpenAI’s Generative Pre-trained Transformer (GPT), has expanded AI’s influence beyond clinical applications into academic and research domains. GenAI, with its ability to generate coherent, contextually relevant, human-like text, has introduced new possibilities in medical research.[ 11 , 12 , 13 , 14 ] It is increasingly used to assist with the demanding and time-consuming task of academic paper writing.[ 15 ] Despite its growing adoption, there is a notable lack of literature addressing GenAI’s practical applications, limitations, and ethical considerations. At the same time, current journals vary widely in their guidelines regarding GenAI use, reflecting the absence of a unified standard.[ 16 ] This rapid integration into academic writing raises concerns about research ethics and the integrity of scientific publications, highlighting the urgent need for explicit guidelines.[ 11 , 14 , 17 , 18 ] Writing a medical paper is a complex process involving aspects such as idea generation, literature review, data analysis, manuscript drafting, etc., Conventionally, these aspects required professional expertise across disciplines. However, AI-driven tools are now assisting these tasks, boosting productivity and potentially improving academic output quality. With its advanced text-generation capabilities, GenAI offers robust support in these areas, making it a powerful tool for medical paper writing.[ 12 , 19 , 20 , 21 ] This review article explores the critical issues related to the use of GenAI in medical paper writing, with a special focus on ophthalmology. First, we provided a comprehensive overview of current publications on GenAI applications in medical paper writing, covering key applications and considerations, including idea generation, literature review, institutional review board (IRB) preparation, data collection and analysis, image generation, manuscript drafting, writing refinement, and peer review. Second, we evaluate the publicly available specialized AI tools designed for medical paper writing. Finally, we address the limitations and ethical concerns associated with GenAI, along with the effectiveness of current AI detection methods, aiming to provide a framework for the responsible use of GenAI to benefit medical research and the field of ophthalmology. Methods Literature review This is a systematic review that adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. The literature search was conducted on PubMed to identify studies on the application of GenAI in medical paper writing published between January 2020 and December 31, 2024. The search terms, including MeSH terms, were: (“Natural Language Processing” [Mesh] OR “Generative Artificial Intelligence” OR “Generative AI” OR “Large Language Model” OR “AI Chatbots” OR “Generative Adversarial Networks” OR “GPT” OR “Llama” OR “transformers” OR “ChatGPT” OR “Variational Autoencoder” OR “Multimodal model”) AND (“Writing” [Mesh] OR “Publishing” [Mesh] OR “Publications” [Mesh]) AND (“Ophthalmology” OR “Medicine” OR “medical” OR “health” OR “health care” OR “healthcare system”). The study selection process involved a two-stage screening. In the initial stage, three independent reviewers–FY Hu, PJ Cheng, and LY Chen–screened titles and abstracts based on predefined inclusion and exclusion criteria. Discrepancies were resolved through discussion until consensus was reached. In the second stage, the same three reviewers conducted a full-text review to confirm the studies’ eligibility for inclusion. Inclusion criteria focused on articles discussing the development, application, benefits, challenges, and ethical considerations of GenAI in medical paper writing, with particular emphasis on ophthalmology. Only articles published after January 2020, coinciding with the release of ChatGPT-3, were included, without restrictions on article type. Articles without an English abstract or full-text availability and those lacking detailed insights were excluded. Data extraction was carried out by the three reviewers using a standardized form, capturing study characteristics, GenAI models/tools discussed, research aims, applications, benefits, challenges, ethical considerations, guidelines, and ophthalmology-specific content. The findings were synthesized narratively to provide an overview of GenAI applications in medical paper writing. Articles were categorized into nine considerations and applications, including idea generation, literature review, IRB preparation, data collection, data analysis, image generation, manuscript drafting, writing refinement, and peer review. Descriptive statistics were used to calculate the frequency of mentions [ Table 1 ]. Table 1. Frequency of mention for 9 key applications and considerations in the literature on the use of generative artificial intelligence in medical paper writing Applications and considerations Frequency/Total (%) References Idea generation 9/73 (12%) [ 11 , 14 , 19 , 22 , 23 , 24 , 25 , 26 , 27 ] Literature review 24/73 (33%) [ 14 , 18 , 22 , 26 , 27 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ] Institutional review board document preparation 0/73 (0%) Not mentioned Data collection 6/73 (8%) [ 39 , 41 , 43 , 44 , 48 , 49 ] Data analysis 10/73 (14%) [ 11 , 14 , 19 , 21 , 22 , 23 , 26 , 28 , 44 , 50 ] Manuscript drafting 25/73 (34%) [ 11 , 12 , 13 , 14 , 18 , 19 , 20 , 22 , 23 , 24 , 25 , 27 , 28 , 47 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 ] Writing refinement 18/73 (25%) [ 11 , 14 , 18 , 22 , 25 , 26 , 44 , 46 , 50 , 51 , 52 , 55 , 58 , 59 , 62 , 63 , 64 , 65 ] Image generation 2/73 (3%) [ 66 , 67 ] Peer review 11/73 (15%) [ 14 , 22 , 23 , 28 , 47 , 53 , 67 , 68 , 69 , 70 , 71 ] Open in a new tab Artificial intelligence tools for medical paper writing, image generation, as well as tools for detecting artificial intelligence-generated content An in-depth exploration of online resources was conducted to identify a range of AI-driven tools, including those for research, productivity, image generation, and AI detection, as well as applications with significant potential for medical research. The objective was to evaluate the application of these tools across various aspects of academic writing [ Figure 1 ]. Both free and paid tools were included in this exploration. Figure 1. Open in a new tab Summary of the applications and considerations of generative artificial intelligence in medical paper writing, covering nine possible roles from idea generation to peer review, with a comparison of its advantages and limitations. Created with Flaticon.com and BioRender.com Results Literature review For the systematic review on GenAI in medical paper writing, 377 articles were initially retrieved, with 3 duplicates removed. After initial screening, 58 were excluded for missing abstracts, 196 for irrelevant focuses, leaving 120 for full-text evaluation. Ultimately, 73 articles were included [ Figure 2 and Supplementary Table 1 ]. Figure 2. Open in a new tab Flowchart describing the study selection process Supplementary Table 1. Overview and characteristics of the included articles Reference First author Date Article type Main GenAI tools discussed Applications or considerations discussed [ 11 ] Arshad 2023 Review article ChatGPT A, E, F, G [ 12 ] Brameier 2023 Perspective LLMs in general J [ 13 ] Nagarkar 2023 Editorial ChatGPT-3 J [ 14 ] Salimi 2023 Perspective ChatGPT A, B, E, F, G, I, K [ 15 ] Mishra 2024 Original article LLMs in general L [ 17 ] Lee 2023 Review article LLMs in general J [ 18 ] Semrl 2023 Opinion article ChatGPT B, F, G [ 19 ] Altmäe 2023 Commentary ChatGPT A, E, F [ 20 ] Benichou 2023 Brief communication ChatGPT F [ 21 ] Oduoye 2023 Perspective ChatGPT E [ 22 ] Carobene 2024 Opinion article GPT 4 A, B, E, F, G, I [ 23 ] Lin 2024 Opinion article LLMs in general E, F, I [ 24 ] Margetts 2024 Original article ChatGPT 4.0 A, F [ 25 ] Costa 2024 Review article ChatGPT A, F, G [ 28 ] Inam 2024 Review article LLMs in general E, F, I [ 51 ] Tang 2024 Original article LLMs in general F, G [ 26 ] Ruksakulpiwat 2024 Original article ChatGPT A, B, E, G [ 29 ] Demir 2024 Original article GPT-3.5/4.0 B [ 30 ] Waisberg 2023 Letter to the editor GPT-4 B [ 31 ] Qureshi 2023 Commentary ChatGPT B [ 32 ] Jenko 2024 Original article GPT-4, the-literature.com B [ 33 ] Teperikidis 2024 Special article (tutorial) ChatGPT 4.0 B [ 34 ] Hsu 2024 Original article ChatPDF B [ 35 ] Oami 2024 Original article GPT-4 Turbo B [ 36 ] Dennstädt 2024 Original article FlanT5, OHNC, Mixtral, Platypus 2 B [ 37 ] Guo 2024 Original article GPT-4 B [ 38 ] Issaiy 2024 Original article GPT-3.5 B [ 39 ] Tran 2024 Original article GPT-3.5 Turbo B, D [ 40 ] Landschaft 2024 Original article GPT-4 B [ 41 ] Górska 2024 Original article Four biomedical LLMs B, D [ 42 ] Matsui 2024 Original article GPT-3.5, GPT-4 B [ 43 ] Khraisha 2024 Original article GPT-4 B, D [ 44 ] Luo 2024 Perspective LLMs in general B, D, E, G [ 45 ] Sanii 2024 Original article ChatGPT, Perplexity.AI B [ 48 ] Reason 2024 Original article GPT-4o D [ 49 ] Gartlehner 2024 Original article Claude 2 D [ 62 ] Hwang 2023 Perspective LLMs in general G, K [ 50 ] Ramoni 2024 Review article LLMs in general E, G [ 52 ] Babl 2023 Original article ChatGPT F, G [ 53 ] Leung 2023 Editorial LLMs in general F, I, K [ 54 ] Verhoeven 2023 Viewpoint ChatGPT F [ 55 ] Nazzal 2024 Original article ChatGPT 4.0 F, G [ 56 ] Kacena 2024 Original article ChatGPT 4.0 F [ 57 ] Awosanya 2024 Review article ChatGPT F [ 58 ] Chou 2024 Original article ChatGPT F, G [ 59 ] Alyasiri 2024 Original article ChatGPT-4 F, G [ 60 ] Li 2024 Original article ChatGPT-3.5, ChatGPT-4 F [ 63 ] Dergaa 2024 Original article ChatGPT-3.5 G [ 64 ] Pividori 2024 Brief communication Self-developed LLM-integrated platform G [ 65 ] Bellini 2024 Opinion article GPT-4.0 G, K [ 46 ] Ellaway 2023 Editorial LLMs in general B, G [ 66 ] Ho 2023 Systematic Review and Meta-Analysis ChatGPT H [ 67 ] Kaebnick 2023 Editorial LLMs in general H, I [ 114 ] Sonmez 2024 Review article LLMs in general H [ 68 ] Daungsupawong 2024 Correspondence LLMs in general I [ 47 ] Kadi 2024 Original article ChatGPT 3.0 and 4.0 B, F, I [ 69 ] Suleiman 2024 Original article GPT-4 I [ 70 ] Zielinski 2023 Editorial ChatGPT I [ 27 ] Salvagno 2023 Perspective ChatGPT A, B, F [ 61 ] Rahimi 2024 Opinion article ChatGPT J [ 122 ] Levin 2024 Original article ChatGPT (GPT-3.5) K [ 124 ] Habibzadeh 2023 Original article ChatGPT K [ 125 ] Liu 2023 Commentary ChatGPT J, K [ 126 ] Flitcroft 2024 Original article ChatGPT K [ 127 ] Májovský 2024 Perspective LLMs in general K [ 71 ] von Wedel 2024 Research letter GPT-3.5 I [ 128 ] Rahimi 2023 Opinion article ChatGPT F [ 129 ] Hassanipour 2024 Original article ChatGPT J [ 130 ] Silva 2023 Perspective ChatGPT J [ 131 ] Carnino 2024 Original article LLMs in general J [ 132 ] Ganjavi 2024 Original article LLMs in general J [ 133 ] Barrington 2023 Review article ChatGPT J [ 134 ] Teixeira da Silva 2024 Correspondence ChatGPT J Open in a new tab Categories of applications or considerations: A=Idea generation, B=Literature review, C=Institutional review board document preparation, D=Data collection, E=Data analysis, F=Manuscript drafting, G=Writing refinement, H=Image generation, I=Peer review, J=Ethical concerns, K=AI-generated content detection, L=Others. GenAI=Generative artificial intelligence, LLM=Large language model All selected articles were reviewed to determine whether they addressed the nine considerations and applications mentioned in Table 1 . This review demonstrated the frequencies with which these topics were mentioned in the included articles. Artificial intelligence tools for medical paper writing, image generation, as well as tools for detecting artificial intelligence-generated content Artificial intelligence tools for paper writing Two main categories of AI tools for medical paper writing were found, including non-GenAI [ Table 2 ] and GenAI tools [ Table 3 ]. Table 2. Nongenerative artificial intelligence tools for medical paper writing Applications Main features Logo Categories Semantic Scholar[ 72 ] Extensive database coverage Article impact analysis Integration of multi-platforms B Dimensions[ 73 ] Extensive database coverage Altmetric analysis Integration of multi-platforms B Scite[ 74 ] Citation statement database Citation context analysis Visualization of citations B Elicit[ 75 ] Summarizing papers of interest Relevant citation prioritization Research workflows automation B Research Rabbit[ 76 ] Visualization of publications Literature recommendations Enabling collaboration B Julius[ 77 ] High accessibility Data visualization Allowing multiple data sources E IBM Watsonx[ 78 ] Large-scale data analysis Automated data preparation Supporting multiple engines E Open in a new tab Categories: A=Idea generation, B=Literature review, C=Institutional review board document preparation, D=Data collection, E=Data analysis, F=Manuscript drafting, G=Writing refinement, H=Image generation Table 3. Generative artificial intelligence tools for medical paper writing Applications Main features Logo Categories ChatGPT[ 79 ] Brainstorming of ideas Creating preliminary drafts Content extraction Data analysis and adjustment Paraphrasing and rewriting A, C, D, E, F, G Claude[ 80 ] Brainstorming of ideas Creating preliminary drafts Comprehensive summarization Data analysis and adjustment Paraphrasing and rewriting A, E, F, G DeepL[ 81 ] Grammar correction Tone adjustment Alternative word suggestion G Grammarly[ 82 ] Grammar correction Tone adjustment Improving sentence clarity G QuillBot[ 83 ] Advanced paraphrasing Grammar correction Sentence structuring G NotebookLM[ 84 ] Content extraction Comprehensive summarization Data analysis and adjustment D, E Consensus[ 85 ] Literature Collection Content extraction Comprehensive summarization Data analysis and adjustment B, D, E STORM[ 86 ] Information sourcing Creating preliminary drafts Data analysis and adjustment D, E, F Perplexity[ 87 ] Cited source retrieval Real-time fact-checking B DALL·E 3[ 88 ] Text-to-image generation Integration with ChatGPT Inpainting for graph modifying H Midjourney[ 89 ] Text-to-image generation Providing various artistic styles High-resolution outputs H MyLens[ 90 ] Project and strategy planning Mind maps, flowcharts creation Flexible content creation H Open in a new tab Categories: A=Idea generation, B=Literature review, C=Institutional review board document preparation, D=Data collection, E=Data analysis, F=Manuscript drafting, G=Writing refinement, H=Image generation The non-GenAI tools we found include Semantic Scholar,[ 72 ] Dimensions,[ 73 ] Scite,[ 74 ] Elicit,[ 75 ] and Research Rabbit.[ 76 ] These tools are useful for literature review, improving search efficiency, contextual analysis, generating search terms, summarizing articles, extracting key information, and assisting in systematic review screening. We also found tools related to data analysis, including Julius AI[ 77 ] and IBM Watsonx,[ 78 ] which are claimed to be used for processing large data sets, identifying trends, improving accuracy, and suggesting appropriate methods for analysis. The GenAI tools we identified include Claude,[ 80 ] DeepL,[ 81 ] Grammarly,[ 82 ] QuillBot,[ 83 ] NotebookLM,[ 84 ] Consensus,[ 85 ] STORM,[ 86 ] Perplexity,[ 87 ] and ChatGPT.[ 79 ] These tools contribute to medical writing through a variety of applications, including idea generation, literature review, data analysis, manuscript drafting, and writing refinement. Claude is notable for its capabilities in idea generation, content extraction, and paraphrasing. DeepL, Grammarly, and QuillBot focus on grammar correction and sentence structuring. NotebookLM and Consensus assist with literature collection and summarization, helping researchers efficiently extract and synthesize key findings from relevant studies. STORM supports literature review by retrieving, analyzing, and synthesizing data from multiple sources, structuring it into well-organized drafts. It also facilitates data analysis by processing and summarizing complex information, making it useful for research insights and evidence synthesis. Perplexity focuses on cited source retrieval, ensuring accuracy, and credibility. ChatGPT,[ 79 ] a GPT-based model, shows versatility across tasks. Beyond the aforementioned functions common to most GenAI tools, it offers other powerful features, including idea generation, assisting with IRB preparation, data collection, and manuscript drafting. Artificial intelligence tools for image generation For image generation, we found DALL·E 3,[ 88 ] Midjourney,[ 89 ] and MyLens.[ 90 ] These applications are designed to facilitate the creation of visuals, customized illustrations, and the construction of mind maps or flowcharts. Artificial intelligence-generated content detection tools For AI-generated content detection, we identified several websites, including Copyleaks[ 91 ] and DetectGPT,[ 92 ] as well as commercial systems such as Turnitin[ 93 ] and PlagiarismCheck.[ 94 ] In addition, we found AI tools such as GPTZero[ 95 ] that are commonly used for AI-generated text detection. Some GenAI tools like QuillBot[ 83 ] and Grammarly[ 82 ] also have built-in AI detection features to assist in AI content analysis. Furthermore, we also identified relevant literature that has tested the effectiveness of these tools. Discussion The writing and publication of a medical research paper involves several aspects [ Figure 1 ], requiring significant human effort, expert knowledge, and specialized applications. Prior to the advent of GenAI, several AI-driven research and productivity (non-GenAI) tools had already been assisting with these tasks, though their impact was limited.[ 96 ] However, with the introduction of GenAI, particularly large language models like ChatGPT, this process has undergone a significant leap forward. GenAI can assist researchers across various stages of medical paper writing, including idea generation, literature review, IRB preparation, data collection and analysis, image generation, manuscript drafting, and writing refinement. Along with this, we need to consider the issues related to peer review, inherent limitations, and ethical considerations [ Figure 1 ]. This is particularly valuable for ophthalmic research articles, as ophthalmology research encompasses advanced foundational studies, pharmacological treatments, surgical techniques, and the handling of extensive data and images.[ 97 , 98 , 99 , 100 , 101 ] Nine key applications and considerations Idea generation The most crucial aspect of a medical research paper is idea generation, emphasizing the unique value of human creativity. This process requires a deep understanding of the field, particularly in identifying clinical needs, proposing hypotheses, and developing feasible research methods. GenAI can support this by searching databases, organizing ideas, offering insights, and suggesting relevant literature.[ 11 , 19 , 22 , 23 , 24 , 25 ] However, overreliance on GenAI may stifle creativity.[ 22 , 23 , 24 , 25 , 28 ] Balancing its use with critical thinking is essential to maintain originality. Although there is some literature addressing this topic,[ 22 , 28 , 51 ] there remains a lack of large-scale studies that thoroughly analyze it. This gap underscores the need for further research to fully understand the implications and potential of integrating GenAI into the idea-generation process. Literature review The medical literature review is crucial for establishing the research background, identifying gaps, avoiding duplication, and referencing methods, especially in systematic reviews. Databases such as PubMed, Cochrane Library, EMBASE, and Google Scholar are indispensable, while tools such as MeSH, Emtree, and Boolean operators enhance systematic article searches. Research databases such as Semantic Scholar,[ 72 ] Dimensions,[ 73 ] Scite,[ 74 ] Elicit,[ 75 ] and Research Rabbit[ 76 ] now use natural language processing to enhance searches, offering features such as citation trends, context highlights, progress timelines, abstract summaries, and relevant article identification. In the era of GenAI, chatbots like ChatGPT streamline literature reviews by generating search terms,[ 26 , 29 ] searching databases, summarizing, translating, extracting key information,[ 18 , 22 , 30 , 31 , 32 , 33 , 34 , 35 ] and assisting in title and abstract screening.[ 33 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ] Several recent studies have evaluated the accuracy and effectiveness of ChatGPT and other chatbots as literature screening tools.[ 38 , 39 , 40 , 102 , 103 ] While most findings reported lower accuracy compared to human reviewers, improvements have been observed in newer iterations. Overall, the literature reflects that GenAI shows promise in assisting systematic reviews.[ 104 ] With rapid and extensive training, the updated versions may significantly and quickly improve their accuracy. GenAI tools are not without drawbacks, such as hallucinations and incorrect citations, making cross-checking essential.[ 24 , 26 , 33 , 45 , 51 ] Some tools now provide web links to original sources to reduce errors. GenAI cannot fully replace manual literature reviews but significantly accelerates the process, with summarization and translation features especially aiding non-English speakers, who spend 90.8% more time reading scientific articles, according to Amano et al .[ 105 ] Institutional review board preparation The IRB ensures ethical standards in human research. However, preparing applications can be labor-intensive, especially for early-career researchers. Integrating GenAI enhances efficiency and accuracy by providing reliable references, simplifying technical language, and improving clarity. GenAI connects to databases like PubMed via an application programming interface (API) for reliable references, simplifies technical language for IRB members from diverse backgrounds, and enables researchers to focus on ethical considerations. However, the literature on this topic is quite limited.[ 106 ] Data collection GenAI tools have significant potential in data collection by organizing and processing the provided data. With strong capabilities in labeling texts and extracting elements from large datasets,[ 39 , 44 , 48 , 49 ] GenAI can efficiently extract, label, categorize, and present data in more readable formats, such as bullet points or tables. Recent studies have shown the promising performance of GenAI models like GPT-4 in data extraction for systematic literature reviews.[ 103 , 104 ] Fine-tuned GenAI models also demonstrated the capability to extract ophthalmic examination data from electronic medical records.[ 107 ] In addition, multimodal GenAI tools like ChatGPT-4 can process various file formats, including Excel, Word, PDF, and images, making them particularly useful in ophthalmic research, where large volumes of nontext-based data need to be processed. However, it is crucial to be aware that data uploaded to ChatGPT or other cloud servers may become part of its training data,[ 23 , 62 ] raising potential patient privacy concerns–a critical issue in health care. To minimize privacy breaches, personally identifiable information, such as the 18 identifiers outlined by HIPAA,[ 108 ] should be removed before uploading patient data. Alternatively, using GenAI models within a private domain, carefully monitored to ensure data safety, is recommended.[ 23 ] Despite the importance of this issue, there is currently little literature discussing the regulations and concerns in this area. Data analysis The way data is analyzed significantly influences research outcomes, playing a key role in verifying hypotheses, answering research questions, and uncovering trends that add credibility to results. It is crucial that statistical methods and data analysis techniques align with the research objectives. AI-driven tools like Julius AI[ 77 ] and IBM Watsonx[ 78 ] offer more efficient and precise analysis than traditional methods, automating complex statistical processes, reducing human error, and handling large datasets for faster and more accurate insights. GenAI chatbots like ChatGPT-4 are also able to quickly process vast datasets to identify patterns, trends, and correlations, aiding in hypothesis generation and improving accuracy by minimizing human errors.[ 14 , 21 , 22 , 44 ] They can analyze various data formats, such as Excel files and images, suggest appropriate analysis methods,[ 11 , 19 ] and generate codes for statistical analyses.[ 50 ] These capabilities are especially valuable in medical research, where large amounts of patient data are common. A notable application in ophthalmology used generative adversarial networks (GANs) to enhance clinical trial power by generating synthetic visual field data. When combined with real records, these data enabled more sensitive detection of disease progression with smaller sample sizes. Though structurally different from LLMs, GANs are also a form of GenAI, offering practical advantages in dataset augmentation, longitudinal simulation, and trial design refinement for ophthalmic research.[ 109 ] As mentioned in the ‘4.1.4. Data collection section, special attention must be given to ensure that all personally identifiable information in patient data is properly protected to maintain data privacy and security. Manuscript drafting GenAI has transformed the process of paper writing by generating initial drafts based on topics or materials provided by users, significantly reducing the time needed to produce high-quality content.[ 12 , 18 , 19 , 20 , 22 , 25 , 52 , 53 , 54 ] Its writing ability is often comparable to, or even better than, that of the average individual.[ 55 ] Several recent studies compared the two primary modes of GenAI use in manuscript drafting–AI-only (AIO) and AI-assisted (AIA) –with traditional human-only drafting.[ 24 , 55 , 56 , 57 ] The AIO mode, where drafts are entirely written by ChatGPT, is the fastest of the three but often results in more incorrect references and superficial content, necessitating more editing and fact-checking. The AIA mode, where GenAI generates drafts based on references provided by humans, is also quicker than human-only drafting but carries the highest risk of plagiarism. Despite the need for thorough human review and editing, using GenAI for initial drafts can still enhance overall efficiency. This is particularly beneficial for those less proficient in English and writing techniques[ 24 , 52 , 58 , 59 , 60 ] as non-native English speakers typically spend 50.6% more time writing a paper in English compared to native speakers.[ 105 ] Regarding domain-specific manuscript drafting in ophthalmology, GenAI also shows promise. While direct evaluations of the quality of GenAI-generated research manuscripts remain limited, recent studies have shown that models such as GPT-4 are capable of generating ophthalmic operative notes and patient information leaflets with appropriate technical language and structured coherence, suggesting their potential for assisting in writing specialized sections of ophthalmology manuscripts.[ 110 , 111 ] Writing refinement In academic writing, precise grammar and specialized terminology are crucial, especially for non-English-speaking researchers who often rely on editing services to ensure accuracy and fluency. GenAI plays a key role in refining manuscripts by improving grammar, terminology, content organization, and tone consistency, enhancing clarity and readability.[ 11 , 22 , 25 , 26 , 50 , 55 , 58 , 63 , 64 ] It also helps bridge language barriers through accurate translation and correction, making academic communication more accessible globally.[ 46 , 50 , 52 , 59 , 62 , 65 ] While ChatGPT has been widely adopted for these purposes, several AI-driven tools specialized in writing refinement, such as DeepL,[ 81 ] Grammarly,[ 82 ] and QuillBot,[ 83 ] have also integrated GenAI technology to enhance their capabilities. GenAI tools make the editing process more efficient, ensuring manuscripts meet publication standards[ 22 , 50 ] and addressing the challenges faced by non-native English speakers. These tools help reduce language-based inequalities, allowing researchers to focus more on their expertise.[ 62 , 65 ] Image generation In addition to text-to-text GenAI models, text-to-image GenAI models can be used to create visuals from data, generate illustrations from text prompts, and even produce demonstration videos.[ 66 , 67 ] This is especially beneficial for ophthalmology articles, which often require a variety of visuals such as flowcharts, diagrams, mind maps, statistical graphs, examination images, and even surgical videos. Currently, various GenAI tools can effectively generate these visual elements to support academic writing, including DALL·E[ 88 ] (integrated with ChatGPT) and Midjourney[ 89 ] for image creation, as well as MyLens[ 90 ] for generating timelines and mind maps. Furthermore, specialized GenAI models are increasingly applied to produce specific ophthalmological images. For instance, models can generate angiography images from retinal fundus photographs and synthetic optical coherence tomography (OCT) images of retinal disorders.[ 112 , 113 ] Beyond generation, deep learning approaches can also enhance existing OCT images by removing shadows, thereby improving image quality for analysis and presentation.[ 2 ] These advancements underscore GenAI’s utility in creating and refining high-quality, specialized visuals essential for ophthalmology publications. However, widely available text-to-image models often lack the specialized medical knowledge needed for realistic visuals,[ 114 ] leading to both factual and terminological errors, as seen in a recently retracted article.[ 115 ] This highlights the need for specialized models and raises ethical concerns in using AI-generated images to mimic real patient results, which must be carefully addressed. Peer review The use of GenAI in peer review is controversial. While it could help address the increasing demand for reviewers by performing rapid screening, flagging potential issues, and verifying adherence to guidelines,[ 97 , 68 ] its current limitations in accuracy and reliability make it unsuitable as a primary reviewer.[ 47 ] Recent studies showed that ChatGPT exhibits low concordance with human reviewers in the peer review process.[ 69 ] Ethical concerns about confidentiality and data privacy further complicate its use.[ 23 , 53 , 70 ] Several academic journals, including JAMA, The Lancet, and Science, have prohibited the submission of manuscripts to AI software. Integrating GenAI into peer review will require stringent regulations and comprehensive guidelines. Artificial intelligence tools for medical paper writing, image generation, as well as tools for detecting artificial intelligence-generated content Artificial intelligence tools for paper writing We explored non-GenAI and GenAI, the two main categories of AI tools, for medical paper writing in our research. Non-GenAI tools play an important role in literature review today. AI-powered databases, including Semantic Scholar,[ 72 ] Dimensions,[ 73 ] and Scite,[ 74 ] offer multiple search strategies based on researchers’ preferences. Tools such as Elicit[ 75 ] and Research Rabbit[ 76 ] allow for literature visualization and integration, thus providing a clearer view of research. They streamline the literature review process by enhancing search efficiency and contextual analysis. In addition to literature search platforms, domain-specific language models such as BioBERT and PubMedBERT are pre-trained on biomedical corpora like PubMed and PubMed Central (PMC), making them more tailored to the biomedical domain.[ 116 , 117 ] For data analysis, Julius AI[ 77 ] and IBM Watsonx[ 78 ] offer advanced statistical capabilities, with Julius AI enabling real-time interaction and Watsonx handling more complex datasets, providing deeper insights and data interpretation. As AI technology continues evolving, GenAI tools are becoming increasingly beneficial for literature review and medical research. NotebookLM[ 84 ] has emerged as a valuable tool for organizing and summarizing key findings from multiple sources, helping researchers efficiently synthesize extensive literature. Consensus AI[ 85 ] enhances the research process by retrieving and aggregating academic studies, providing evidence-based insights that support more informed writing and critical analysis. Meanwhile, STORM[ 86 ] from Stanford University assists not only in literature review but also in data analysis, allowing researchers to extract meaningful insights from complex datasets. In addition, Perplexity AI[ 87 ] plays a crucial role in real-time fact-checking, citation retrieval, and contextual explanations, enhancing both accuracy and depth in medical paper writing. Beyond literature review, writing refinement is another area where AI tools have become increasingly sophisticated. QuillBot,[ 83 ] DeepL,[ 81 ] and Grammarly[ 82 ] help improve writing quality by enhancing grammar, readability, and tone consistency. QuillBot is noted for its strengths in paraphrasing and language refinement, making it especially effective for adjusting tonality in medical paper writing. DeepL and Grammarly were initially known for their precise translation and language accuracy enhancements. In their latest updates, they have integrated GenAI features, which go beyond simple language translation or grammar corrections to enrich the overall quality of the writing process. GPT-based models, such as ChatGPT,[ 79 ] offer versatile functions. In addition to data analysis and writing refinement, they are capable of collecting, labeling, and categorizing data, presenting it in a clear and organized format. They also assist with organizing ideas, generating insights, assisting in IRB document writing, conducting preliminary research, and drafting manuscripts. The latest version, GPT-4o, released by OpenAI on May 13, 2024, may further enhance its capabilities.[ 118 ] Other domain-specific models such as BioGPT, which is trained on biomedical literature including PubMed abstracts, have demonstrated superior performance in generating biologically accurate summaries and understanding technical medical contexts. This makes it particularly suitable for biomedical manuscript drafting and refining tasks, especially in fields like ophthalmology that require high terminological precision.[ 119 ] Artificial intelligence tools for image generation Image generation is another key feature of GenAI. Tools such as Midjourney[ 89 ] and DALL·E 3[ 88 ] (integrated with ChatGPT) can generate high-resolution images based on prompts. The inpainting feature allows fine-tuning of specific areas of an image. In addition, applications like MyLens[ 90 ] can generate corresponding mind maps, flowcharts, and timelines based on conceptual input. These tools are essential for visualizing research content and improving the clarity of presentations. Although few studies have analyzed the outcomes and potential risks associated with the use of these AI tools,[ 27 , 61 ] the academic community should pay greater attention to these issues and apply the tools wisely and ethically. Artificial intelligence-generated content detection tools Current GenAI models lack tools to assess the accuracy and uncertainty of generated content.[ 120 , 121 ] Detection methods–human review,[ 122 ] AI detection tools,[ 53 , 65 , 123 , 124 , 125 ] and potential watermarking by the GenAI developers[ 62 ] –vary in effectiveness, with no reliable solution yet established, highlighting the need for ongoing evaluation of AI-generated content. Recently, numerous AI detection tools have been developed to enhance detection accuracy, including publicly available websites like Copyleaks,[ 91 ] DetectGPT,[ 92 ] and GPTZero,[ 95 ] commercial systems such as Turnitin[ 93 ] and PlagiarismCheck,[ 94 ] and GenAI tools with built-in AI detectors, including QuillBot[ 83 ] and Grammarly.[ 83 ] However, most studies still concluded that the currently available tools remain unreliable.[ 123 , 126 ] Weber-Wulff et al .[ 123 ] evaluated 14 detection tools, including Turnitin, PlagiarismCheck, and GPTZero, while Flitcroft et al .[ 126 ] tested OpenAI AI classifier, Content at Scale, and Originality. AI. Both studies revealed significant limitations of the current AI-generated content detection tools. Additionally, as detection capabilities improve, they may inadvertently contribute to the refinement of GenAI writing models.[ 127 ] Continuous validation and refinement are essential for any AI detection system. Despite these limitations, authors and reviewers should familiarize themselves with these tools and use them as complementary aids to human identification. Limitations and ethical issues GenAI holds potential for medical writing but also faces challenges related to inherent model limitations and ethical concerns. Limitations such as reliance on outdated or biased training data,[ 11 , 18 , 26 , 27 , 28 , 51 , 57 , 61 , 71 ] inaccuracies, and hallucinations (e.g., nonsensical content, fabricated data, and references),[ 12 , 24 , 28 , 33 , 45 , 50 , 51 , 55 , 128 ] as well as the black-box nature of its decision-making process[ 11 , 39 , 45 ] compromise its reliability. GenAI also raises concerns about originality, copyright, and data privacy.[ 20 , 51 , 53 ] Its tendency to replicate existing content can lead to plagiarism,[ 13 , 19 , 24 , 51 , 55 , 56 , 129 ] while the lack of anonymization in its diverse training data increases the risk of privacy breaches.[ 23 , 62 ] Overreliance on GenAI may contribute to plagiarism and hinder critical thinking and creativity, particularly among new researchers.[ 22 , 54 , 47 , 125 , 130 , 131 ] Ethical training and careful use are needed to avoid these problems. Furthermore, access to advanced GenAI tools is often unequal, resulting in disparities in research quality, especially for those having less resources or working in less common languages.[ 27 , 61 ] Finally, fabricated images generated by GenAI can also harm clinical diagnoses and research integrity. To solve these issues, journals should establish clear guidelines for using GenAI.[ 16 , 132 ] Transparency about GenAI usage is crucial to understand its limitations and risks.[ 18 , 51 , 61 , 133 , 134 ] Instead of banning AI tools, journals can encourage responsible use, enabling researchers to benefit from GenAI while ensuring research quality and reliability.[ 17 , 23 , 51 , 62 , 70 ] Several ophthalmology journals, including Ophthalmology and JAMA Ophthalmology, have already taken steps in this direction by issuing explicit policies on GenAI use. These typically prohibit listing GenAI as an author and require full disclosure of its role in content generation, thereby fostering accountability and ethical adoption.[ 14 ] More studies are needed to guide ethical and proper use of GenAI in medical writing. Conclusion GenAI has revolutionized medical paper writing in many aspects, with an increasing number of AI tools specifically designed for this purpose being continuously developed. However, GenAI has its limitations and raises potential ethical concerns in the medical field. Authors should use GenAI as a supplementary tool with careful human oversight, while reviewers should exercise caution in its application. Publishers and journals must establish clear, globally consistent guidelines that evolve with technological advancements, and AI developers should encourage interdisciplinary collaboration to refine GenAI models. In conclusion, GenAI’s impact on medical paper writing is substantial and will continue to grow as new models emerge. Data availability statement Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study. Conflicts of interest The authors declare that there are no conflicts of interest of this paper. 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Data Availability Statement Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study. 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